OpenRaiser/Intern-Atlas huggingface dataset:
Computer activity learning from system call time series
Venue: arXiv cs.CR 2017
Year: 2017

Using a previously introduced similarity function for the stream of system calls generated by a computer, we engineer a program-in-execution classifier using deep learning methods. Tested on malware classification, it significantly outperforms current state of the art. We provide a series of performance measures and tests to demonstrate the capabilities, including measurements from production use. We show how the system scales linearly with the number of endpoints. With the system we estimate the total number of malware families created over the last 10 years as 3450, in line with reasonable economic constraints. The more limited rate for new malware families than previously acknowledged implies that machine learning malware classifiers risk being tested on their training set; we achieve F1 = 0.995 in a test carefully designed to mitigate this risk.
ADS: Adaptive and Dynamic Scaling Mechanism for Multimedia Conferencing Services in the Cloud
Venue: arXiv cs.MM 2017
Year: 2017

Multimedia conferencing is used extensively in a wide range of applications, such as online games and distance learning. These applications need to efficiently scale the conference size as the number of participants fluctuates. Cloud is a technology that addresses the scalability issue. However, the proposed cloud-based solutions have several shortcomings in considering the future demand of applications while meeting both Quality of Service (QoS) requirements and efficiency in resource usage. In this paper, we propose an Adaptive and Dynamic Scaling mechanism (ADS) for multimedia conferencing services in the cloud. This mechanism enables scalable and elastic resource allocation with respect to the number of participants. ADS produces a cost-efficient scaling schedule while considering the QoS requirements and the future demand of the conferencing service. We formulate the problem using Integer Linear Programming (ILP) and design a heuristic for it. Simulation results show that ADS mechanism elastically scales conferencing services. Moreover, the ADS heuristic is shown to outperform a greedy algorithm from a resource-efficiency perspective.
Reactive Integrated Mission and Motion planning
Venue: arXiv cs.RO 2017
Year: 2017

Correct-by-construction manipulation planning in a dynamic environment, where other agents can manipulate objects in the workspace, is a challenging problem. The tight coupling of actions and motions between agents and complexity of mission specifications makes the problem computationally intractable. This paper presents a reactive integrated mission and motion planning for mobile-robot manipulator systems operating in a partially known environment. We introduce a multi-layered synergistic framework that receives high-level mission specifications expressed in linear temporal logic and generates dynamically-feasible and collision-free motion trajectories to achieve it. In the high-level layer, a mission planner constructs a symbolic two-player game between the robots and their environment to synthesis a strategy that adapts to changes in the workspace imposed by other robots. A bilateral synergistic layer is developed to map the designed mission plan to an integrated task and motion planner, constructing a set of robot tasks to move the objects according to the mission strategy. In the low-level planning stage, verifiable motion controllers are designed that can be incrementally composed to guarantee a safe motion planning for each high-level induced task. The proposed framework is illustrated with a multi-robot warehouse example with the mission of moving objects to various locations.
Image Segmentation of Multi-Shaped Overlapping Objects
Venue: arXiv cs.CV 2017
Year: 2017

In this work, we propose a new segmentation algorithm for images containing convex objects present in multiple shapes with a high degree of overlap. The proposed algorithm is carried out in two steps, first we identify the visible contours, segment them using concave points and finally group the segments belonging to the same object. The next step is to assign a shape identity to these grouped contour segments. For images containing objects in multiple shapes we begin first by identifying shape classes of the contours followed by assigning a shape entity to these classes. We provide a comprehensive experimentation of our algorithm on two crystal image datasets. One dataset comprises of images containing objects in multiple shapes overlapping each other and the other dataset contains standard images with objects present in a single shape. We test our algorithm against two baselines, with our proposed algorithm outperforming both the baselines.
Mode Selection Schemes for D2D Enabled Aerial Networks
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we present and evaluate the effect of two mode selection schemes for device to device (D2D) enabled areal netwroks. The two schemes are based on a threshold received signal strength (RSS) and an average threshold D2D distance between two given users to select the D2D mode. While one scheme triggers the D2D mode based on distance values only, the other scheme can trigger D2D mode for larger distances if a minimum RSS value is received, for it to maximize connectivity regions. Numerical results show the advantage of the presented schemes in offloading traffic from aerial platforms and the effect of the environment on the performance of D2D enabled aerial networks.
AppSwitch: Resolving the Application Identity Crisis
Venue: arXiv cs.NI 2017
Year: 2017

Networked applications traditionally derive their identity from the identity of the host on which they run. The default application identity acquired from the host results in subtle and substantial problems related to application deployment, discovery and access, especially for modern distributed applications. A number of mechanisms and workarounds, often quite elaborate, are used to address those problems but they only address them indirectly and incompletely. This paper presents AppSwitch, a novel transport layer network element that decouples applications from underlying network at the system call layer and enables them to be identified independently of the network. Without requiring changes to existing applications or infrastructure, it removes the cost and complexity associated with operating distributed applications while offering a number of benefits including an efficient implementation of common network functions such as application firewall and load balancer. Experiments with our implementation show that AppSwitch model also effectively removes the performance penalty associated with unnecessary data path processing that is typical in those application environments.
Non-uniform time-scaling of Carnatic music transients
Venue: arXiv cs.SD 2017
Year: 2017

Gamakas are an integral aspect of Carnatic Music, a form of classical music prevalent in South India. They are used in ragas, which may be seen as melodic scales and/or a set of characteristic melodic phrases. Gamakas exhibit continuous pitch variation often spanning several semitones. In this paper, we study how gamakas scale with tempo and propose a novel approach to change the tempo of Carnatic music pieces. The music signal is viewed as consisting of constant-pitch segments and transients. The transients show continuous pitch variation and we consider their analyses from a theoretical stand-point. We next observe the non-uniform ratios of time-scaling of constant-pitch segments, transients and silence in excerpts from nine concert renditions of varnams in six ragas. The results indicate that the changing tempo of Carnatic music does not change the duration of transients significantly. We report listening tests on our algorithm to slow down Carnatic music that is consistent with this observation.
Use of Commutativity for Cryptology in Secret Communication
Venue: arXiv cs.SY 2017
Year: 2017

Commutativity of subsystems in cascade connected forms to form larger systems gets worthy to improve noise disturbance properties, stability, robustness and many other properties in system design. In this paper, another benefit of commutativity property is indicated, as far as the author knowledge for the first time, and illustrated by examples. This benefit is the gain of a new and original method for transmission of secret signals when travelling in a transmission channel. Hence, the paper presents an original and alternative method in cryptology. Their results are all validated by illustrative examples and Matlab simulation toolbox Simulink.
The convergence guarantee of the iterative thresholding algorithm with suboptimal feedbacks for large systems
Venue: arXiv cs.IT 2017
Year: 2017

Thresholding based iterative algorithms have the trade-off between effectiveness and optimality. Some are effective but involving sub-matrix inversions in every step of iterations. For systems of large sizes, such algorithms can be computationally expensive and/or prohibitive. The null space tuning algorithm with hard thresholding and feedbacks (NST+HT+FB) has a mean to expedite its procedure by a suboptimal feedback, in which sub-matrix inversion is replaced by an eigenvalue-based approximation. The resulting suboptimal feedback scheme becomes exceedingly effective for large system recovery problems. An adaptive algorithm based on thresholding, suboptimal feedback and null space tuning (AdptNST+HT+subOptFB) without a prior knowledge of the sparsity level is also proposed and analyzed. Convergence analysis is the focus of this article. Numerical simulations are also carried out to demonstrate the superior efficiency of the algorithm compared with state-of-the-art iterative thresholding algorithms at the same level of recovery accuracy, particularly for large systems.
An EEG-based Image Annotation System
Venue: arXiv cs.CV 2017
Year: 2017

The success of deep learning in computer vision has greatly increased the need for annotated image datasets. We propose an EEG (Electroencephalogram)-based image annotation system. While humans can recognize objects in 20-200 milliseconds, the need to manually label images results in a low annotation throughput. Our system employs brain signals captured via a consumer EEG device to achieve an annotation rate of up to 10 images per second. We exploit the P300 event-related potential (ERP) signature to identify target images during a rapid serial visual presentation (RSVP) task. We further perform unsupervised outlier removal to achieve an F1-score of 0.88 on the test set. The proposed system does not depend on category-specific EEG signatures enabling the annotation of any new image category without any model pre-training.
Viewport-aware adaptive 360° video streaming using tiles for virtual reality
Venue: arXiv cs.MM 2017
Year: 2017

360° video is attracting an increasing amount of attention in the context of Virtual Reality (VR). Owing to its very high-resolution requirements, existing professional streaming services for 360° video suffer from severe drawbacks. This paper introduces a novel end-to-end streaming system from encoding to displaying, to transmit 8K resolution 360° video and to provide an enhanced VR experience using Head Mounted Displays (HMDs). The main contributions of the proposed system are about tiling, integration of the MPEG-Dynamic Adaptive Streaming over HTTP (DASH) standard, and viewport-aware bitrate level selection. Tiling and adaptive streaming enable the proposed system to deliver very high-resolution 360° video at good visual quality. Further, the proposed viewport-aware bitrate assignment selects an optimum DASH representation for each tile in a viewport-aware manner. The quality performance of the proposed system is verified in simulations with varying network bandwidth using realistic view trajectories recorded from user experiments. Our results show that the proposed streaming system compares favorably compared to existing methods in terms of PSNR and SSIM inside the viewport.
The ACCompanion v0.1: An Expressive Accompaniment System
Venue: arXiv cs.SD 2017
Year: 2017

In this paper we present a preliminary version of the ACCompanion, an expressive accompaniment system for MIDI input. The system uses a probabilistic monophonic score follower to track the position of the soloist in the score, and a linear Gaussian model to compute tempo updates. The expressiveness of the system is powered by the Basis-Mixer, a state-of-the-art computational model of expressive music performance. The system allows for expressive dynamics, timing and articulation.
Integrating Queuing Regime into Cognitive Radio Channel Aggregation Policies: A Performance Evaluation
Venue: arXiv cs.NI 2017
Year: 2017

Channel aggregation (CA) is one of the newest concept which cognitive radio network is bringing to bear for the smooth role out of fifth/next generation wireless networks. This is the combining of several unused primary user spectrum holes into a logic usable channel. However, several of these strategies have been investigated considering the varying nature of wireless link and adaptive modulation and coding (AMC). Examples are the instant blocking strategy (IBS) and readjustment based strategy (RBS). This paper develops and compares two CA policies with queue, which are the IBS with queue (IBS + Q), and the RBS with queue (RBS+Q). This is in furtherance of previous proposed work. The aim is to identifying the impact of a queuing regime on the performance of the secondary network such that any secondary user (SU) that has not completed its service, as an alternative to dropping or forcibly terminating the service, it is queued in order to get another opportunity to access the primary user (PU) channels. The performance is evaluated through a simulation framework. The results validate that with a welldesigned queuing regime, capacity, access and other metrics can be improved with significant reduction in blocking and forced termination probabilities respectively.
Extractive Multi-document Summarization Using Multilayer Networks
Venue: arXiv cs.CL 2017
Year: 2017

Huge volumes of textual information has been produced every single day. In order to organize and understand such large datasets, in recent years, summarization techniques have become popular. These techniques aims at finding relevant, concise and non-redundant content from such a big data. While network methods have been adopted to model texts in some scenarios, a systematic evaluation of multilayer network models in the multi-document summarization task has been limited to a few studies. Here, we evaluate the performance of a multilayer-based method to select the most relevant sentences in the context of an extractive multi document summarization (MDS) task. In the adopted model, nodes represent sentences and edges are created based on the number of shared words between sentences. Differently from previous studies in multi-document summarization, we make a distinction between edges linking sentences from different documents (inter-layer) and those connecting sentences from the same document (intra-layer). As a proof of principle, our results reveal that such a discrimination between intra- and inter-layer in a multilayered representation is able to improve the quality of the generated summaries. This piece of information could be used to improve current statistical methods and related textual models.
Internalising Interaction Protocols as First-Class Programming Elements in Multi Agent Systems
Venue: arXiv cs.MA 2017
Year: 2017

Since their inception, Multi Agent Systems (MASs) have been championed as a solution for the increasing problem of software complexity. Communities of distributed autonomous computing entities that are capable of collaborating, negotiating and acting to solve complex organisational and system management problems are an attractive proposition. Central to this is the requirement for agents to possess the capability of interacting with one another in a structured, consistent and organised manner. This thesis presents the Agent Conversation Reasoning Engine (ACRE), which constitutes a holistic view of communication management for MASs. ACRE is intended to facilitate the practical development, debugging and deployment of communication-heavy MASs. ACRE has been formally defined in terms of its operational semantics, and a generic architecture has been proposed to facilitate its integration with a wide variety of diverse agent development frameworks and Agent Oriented Programming (AOP) languages. A concrete implementation has also been developed that uses the Agent Factory AOP framework as its base. This allows ACRE to be used with a number of different AOP languages, while providing a reference implementation that other integrations can be modelled upon. A standard is also proposed for the modelling and sharing of agent-focused interaction protocols that is independent of the platform within which a concrete ACRE implementation is run. Finally, a user evaluation illustrates the benefits of incorporating conversation management into agent programming.
Theoretical limitations of Encoder-Decoder GAN architectures
Venue: arXiv cs.LG 2017
Year: 2017

Encoder-decoder GANs architectures (e.g., BiGAN and ALI) seek to add an inference mechanism to the GANs setup, consisting of a small encoder deep net that maps data-points to their succinct encodings. The intuition is that being forced to train an encoder alongside the usual generator forces the system to learn meaningful mappings from the code to the data-point and vice-versa, which should improve the learning of the target distribution and ameliorate mode-collapse. It should also yield meaningful codes that are useful as features for downstream tasks. The current paper shows rigorously that even on real-life distributions of images, the encode-decoder GAN training objectives (a) cannot prevent mode collapse; i.e. the objective can be near-optimal even when the generated distribution has low and finite support (b) cannot prevent learning meaningless codes for data -- essentially white noise. Thus if encoder-decoder GANs do indeed work then it must be due to reasons as yet not understood, since the training objective can be low even for meaningless solutions.
Contaminant Removal for Android Malware Detection Systems
Venue: arXiv cs.CR 2017
Year: 2017

A recent report indicates that there is a new malicious app introduced every 4 seconds. This rapid malware distribution rate causes existing malware detection systems to fall far behind, allowing malicious apps to escape vetting efforts and be distributed by even legitimate app stores. When trusted downloading sites distribute malware, several negative consequences ensue. First, the popularity of these sites would allow such malicious apps to quickly and widely infect devices. Second, analysts and researchers who rely on machine learning based detection techniques may also download these apps and mistakenly label them as benign since they have not been disclosed as malware. These apps are then used as part of their benign dataset during model training and testing. The presence of contaminants in benign dataset can compromise the effectiveness and accuracy of their detection and classification techniques. To address this issue, we introduce PUDROID (Positive and Unlabeled learning-based malware detection for Android) to automatically and effectively remove contaminants from training datasets, allowing machine learning based malware classifiers and detectors to be more effective and accurate. To further improve the performance of such detectors, we apply a feature selection strategy to select pertinent features from a variety of features. We then compare the detection rates and accuracy of detection systems using two datasets; one using PUDROID to remove contaminants and the other without removing contaminants. The results indicate that once we remove contaminants from the datasets, we can significantly improve both malware detection rate and detection accuracy
Block-Sparse Recurrent Neural Networks
Venue: arXiv cs.LG 2017
Year: 2017

Recurrent Neural Networks (RNNs) are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modelling. Sparsity is a technique to reduce compute and memory requirements of deep learning models. Sparse RNNs are easier to deploy on devices and high-end server processors. Even though sparse operations need less compute and memory relative to their dense counterparts, the speed-up observed by using sparse operations is less than expected on different hardware platforms. In order to address this issue, we investigate two different approaches to induce block sparsity in RNNs: pruning blocks of weights in a layer and using group lasso regularization to create blocks of weights with zeros. Using these techniques, we demonstrate that we can create block-sparse RNNs with sparsity ranging from 80% to 90% with small loss in accuracy. This allows us to reduce the model size by roughly 10x. Additionally, we can prune a larger dense network to recover this loss in accuracy while maintaining high block sparsity and reducing the overall parameter count. Our technique works with a variety of block sizes up to 32x32. Block-sparse RNNs eliminate overheads related to data storage and irregular memory accesses while increasing hardware efficiency compared to unstructured sparsity.
The (thin) Bridges of AS Connectivity: Measuring Dependency using AS Hegemony
Venue: arXiv cs.NI 2017
Year: 2017

Inter-domain routing is a crucial part of the Internet designed for arbitrary policies, economical models, and topologies. This versatility translates into a substantially complex system that is hard to comprehend. Monitoring the inter-domain routing infrastructure is however essential for understanding the current state of the Internet and improving it. In this paper we design a methodology to answer two simple questions: Which are the common transit networks used to reach a certain AS? How much does this AS depends on these transit networks? To answer these questions we digest AS paths advertised with the Border Gateway Protocol (BGP) into AS graphs and measure node centrality, that is the likelihood of an AS to lie on paths between two other ASes. Our proposal relies solely on the AS hegemony metric, a new way to quantify node centrality while taking into account the bias towards the partial view offered by BGP. Our analysis using 14 years of BGP data refines our knowledge on Internet flattening but also exhibits the consolidated position of tier-1 networks in today's IPv4 and IPv6 Internet. We also study the connectivity to two content providers (Google and Akamai) and investigate the AS dependency of networks hosting DNS root servers. These case studies emphasize the benefits of the proposed method to assist ISPs in planning and assessing infrastructure deployment.
LatentPoison - Adversarial Attacks On The Latent Space
Venue: arXiv cs.LG 2017
Year: 2017

Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.) can be subject to attacks using a wide variety of exploits. With the advent of scalable deep learning methodologies, a lot of emphasis has been put on the robustness of supervised, unsupervised and reinforcement learning algorithms. Here, we study the robustness of the latent space of a deep variational autoencoder (dVAE), an unsupervised generative framework, to show that it is indeed possible to perturb the latent space, flip the class predictions and keep the classification probability approximately equal before and after an attack. This means that an agent that looks at the outputs of a decoder would remain oblivious to an attack.